In the past, nurses discovered patients fell this way. A noise was heard and then nurses would discover their patient fell, and then provided appropriate care after the event. Falls at home also result in admission to the hospital as a result of patients lying unfound on the floor for hours. By the time they arrive on the hospital division, patients can be severely dehydrated, bleeding, in pain, and malnourished,

Fortunately, advancements in fall prediction, prevention, warning and detection systems have likely reduced the number of falls, especially in hospitals and care settings (Oh-Park et al., 2021). Innovations in video monitoring and pressure sensors, including alarms for beds, chairs, and toilets, alert nurses and families about impending falls. Additionally, recording devices proclaiming messages like, “Call your nurse!” have likely contributed to preventing falls and related injuries. A more recent innovation, the virtual sitter, adds to video monitoring technology by setting parameters via computer, so that a warning alarm will sound when the patient moves, signaling the potential for a fall (Oh-Park et al., 2021).

Earlier fall detection technology for people at home included call systems attached to a wearable device, such as a pendant. When someone falls, they push a button and convey information to a call center, where a messenger dispatches the news to family members and first responders. The days of someone remaining down for hours are now behind us. However, systems like these depend on the notion that people will adhere to wearing the device and be able to access and activate it when needed. According to Fleming and Brayne (2008), 80% of the participants who fell who had call buttons did not use them. Reasons ranged from not wearing the device, becoming unconscious, or simply forgetting to push the button because of the trauma and emotions resulting from the fall.

Innovative fall detection devices were created by collaborating clinicians and engineers to utilize advanced technology in preventing falls. One example includes a wearable device that removes the need to push a button for activation (Kulurkar et al., 2023). This wireless sensor can detect a fall, send the information to a connected smartphone, which uses artificial intelligence (AI) algorithms to determine that a fall has occurred and communicates directly with the victim. Warrington et al. (2021) conducted a fall detection review and found that sensors placed on the trunk, foot, or leg can provide up to 93.1% sensitivity and 86.4% specificity in detecting falls. If the person who falls does not reply, emergency contacts and appropriate first responders are notified. Wearable wireless sensors can be integrated into watches, shoes, belts, or clothing. Furthermore, when people used wearable fall detection technology, they expressed greater confidence in their movement, and were likely to move more. Overall, Warrington et al. (2021) found that wearable technology offers a promising low-cost method to detect falls and access help.

In addition to wearable devices, leveraging other technological advancements contributes to reducing and detecting falls. A network of ambient sensors including cameras, pressure detectors, vibration sensors, and infrared sensors can also be placed throughout the environment as an alternate to a wearable device (Oh-Park et al., 2021). Using motion accelerators, gyroscopes, magnetometers, and Global Positioning System (GPS) technology, sensors detect rapid downward motion and location to recognize a potential fall (Kulurkar et al., 2023). Utilizing a variety of sensors, each capable of detecting multiple factors associated with patient falls, improves the accuracy of preventing and detecting patient falls

So, what comes next in fall detection after wireless technology, AI algorithms and smartphone communication? One possibility includes the use of airbags! Botonis et al. (2022) described sensor technology incorporated into special vests or jackets that detect falls then deploy airbags around the hips and neck, designed to reduce injury. Despite these innovative ideas, more research, collaboration, and innovation is needed for falls prevention and detection advancements. According to Warrington et al. (2021), five research questions related to fall detection technology must be further researched. These questions specifically address the  effectiveness of device use for people with conditions, especially stroke, multiple sclerosis, and age-related frailty.

1. What is the most effective design system that people want to use?
2. Could we use fall detection and alerts to warn of declining balance control?
3. How can we best integrate fall risk assessment into detection technology?
4. How can we best evaluate wearable fall detection technology for extensive studies?
5. What are the most effective algorithms for fall detection and communication?

Answers to these questions may contribute to further reductions in falls, injuries, deaths, and associated costs as well as inspire the next innovations in fall detection.

References

Botonis, O. K., Harari,Y., Embry, K. R., Mummidisetty, C. K., Riopelle, D., Giffhorn, M., Albert, M. V., Heike, V. & Jayaraman, A. (2022). Wearable airbag technology and machine learned models to mitigate falls after stroke. Journal of Neuro Engineering and Rehabilitation, 19(1), 1–14. https://doi.org/10.1186/s12984-022-01040-4

Fleming, J., & Brayne, C. (2008). Inability to get up after falling, subsequent time on floor, and summoning help: Pro-spective cohort study in people over 90 BMJ; 337:a2227. https://doi.org/10.1136/bmj.a2227

Kulurkar, P., Dixit, C. K., Bharathi, V. C., Monikavishnuvarthini, A., Dhakne, A., & Preethi, P. (2023). AI based elderly fall prediction system using wearable sensors: A smart home-care technology with IOT, Measurement: Sensors, 25, 100614, ISSN 2665-9174. https://doi.org/10.1016/j.measen.2022.100614

Oh-Park, M., Doan, T., Dohle, C., Vermiglio-Kohn, V. & Abdou, A. (2021). Technology utilization in fall prevention. American Journal of Physical Medicine & Rehabilitation, 100, 92-99. https://doi.org/10.1097/PHM.0000000000001554

Warrington, D. J., Shortis, E. J., & Whittaker, P. J. (2021). Are wearable devices effective for preventing and detecting falls: An umbrella review (a review of systematic reviews). BMC Public Health, 21(1), 1–12. https://doi.org/10.1186/s12889-021-12169-7